| 摘 要: 在深度学习中，数据是三大核心要素之一。尤其在某些领域，数据的稀有、人工标注造成大量人力的浪费、数据好坏对产出结果的影响，都显现出数据的重要性。鉴于在动漫领域中，人物的制作需要花费大量的人力和时间，所以从动漫头像出发，基于生成对抗网络，结合编码器、残差网络、解码器，经过编码器改变图像的维度，最后利用解码器将提取到的特征数据生成近似于原始图像的数据集。生成对抗网络本身固有的缺点会导致最后的效果并不是很好，于是尝试对生成对抗网络进行深度卷积的改进，再加上WGAN的梯度惩罚思想来优化自编码器基础上的生成对抗网络。 |
| 关键词: 深度学习；生成对抗网络；数据生成；深度卷积 |
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中图分类号: TP391
文献标识码: A
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| Research on Animation Profile Picture Generation based on Generative Adversarial Network |
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PENG Zhanglong
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( School of Computer Science, Yangtze University, Jingzhou 434100, China)
202072671@yangtzeu.edu.cn
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| Abstract: In deep learning, data is one of the three core elements. Especially in some fields, scarcity of data, manpower waste caused by manual labeling, and the impact of data quality on the output results all show the importance of data. As in animation field, production of characters takes a lot of time and manpower, this paper starts from animation profile picture and combines encoder, residual network and decoder based on Generative Adversarial Network. After the encoder changes the dimension of the image, the decoder is used to generate a dataset similar to the original image with extracted feature data. The inherent shortcomings of the Generative Adversarial Network itself will lead to an unideal final effect, so the author tries to improve the Generative Adversarial Network by deep convolution, coupled with the gradient penalty idea of WGAN (Wasserstein Generative Adversarial Network) to optimize the Generative Adversarial Network based on the autoencoder. |
| Keywords: deep learning; Generative Adversarial Network; data generation; deep convolution |
